Univariate and multivariate time series models are being applied to various databases consisting of short series of measurements of serum biochemistries in healthy subjects. The purpose is to gain practical experience in the use of these statistical forecasting techniques for detecting real changes and trends within subjects, taking into account random biological variation and measurement error. The time scale of these series varies from weekly to 6-month and 12-month intervals between observations. Parallel computer-based simulation studies of these models are also underway, particularly to estimate the relative sensitivities and specificities of multivariate and univariate forecasting methods. Mathematical investigations into the asymptotic properties of a new stochastic model of linear change have begun, and this model is being applied to data from patients at the Clinical Center under long-term therapy.